Clinical Outcomes for Patients with Triple Class Exposed Relapsed and Refractory Multiple Myeloma in Alberta, Canada
Bibliographic record
Abstract
Background: Despite advances in treatment options, multiple myeloma (MM) remains generally incurable and most patients become relapsed or refractory to drug classes and require additional lines of therapy. Patients who are considered triple class exposed (TCE) (i.e., treated with three drug classes) may have limited treatment options and poor prognosis once they become relapsed/refractory MM (RRMM). There are limited published data available that examine real world treatment patterns and clinical outcomes in the TCE RRMM patient population. The purpose of this project was to explore Canadian-specific real-world treatment patterns and clinical outcomes in TCE RRMM patients in Canada. Methods: We leveraged multiple, population-based data sources from Alberta, Canada to identify TCE patients. The TCE cohort included individuals who have received a subsequent line of anti-myeloma treatment after prior exposure to a proteasome inhibitor (PI), an immunomodulatory drug (IMiD), and a monoclonal antibody (MAb), regardless of the sequence or combination used. Results: In total,1835 patients were evaluated. The TCE cohort included individuals who have received a subsequent line of anti-myeloma treatment after prior exposure to a PI, an IMiD, and a MAb, regardless of the sequence or combination used. 567 were consistent with the definition of TCE RRMM. Most patients with TCE MM were treated in the modern era (2020-present, 79%) and only 27% were >75 years old. 221 (39%) of TCE RRMM received a subsequent line of therapy. Median time to next therapy (TTNT) in the TCE RRMM was 18.1 months (8.4-13.3) and median TTNT and death was 10.1 months. Post TCE MM therapies will be presented in a Sankey diagram but as expected for the period of evaluation, no BCMA directed therapies were funded and publicly available for treatment of RRMM. Conclusion: The present study provides important insights into the treatment patterns and outcomes of patients with TCE RRMM. The advent of novel approaches such as CART's and bispecific is expected to address an unmet need in this growing population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".